Development of liquid chromatography methods coupled to mass spectrometry for the analysis of substances with a wide variety of polarity in meconium.

International audienceMeconium is the first fecal excretion of newborns. This complex accumulative matrix allows assessing the exposure of the fetus to xenobiotics during the last 6 months of pregnancy. To determine the eventual effect of fetal exposure to micropollutants in this matrix, robust and sensitive analytical methods must be developed. This article describes the method development of liquid chromatography methods coupled to triple quadrupole mass spectrometry for relevant pollutants. The 28 selected target compounds had different physico-chemical properties from very polar (glyphosate) to non-polar molecules (pyrethroids). Tests were performed with three different types of columns: reversed phase, ion exchange and HILIC. As a unique method could not be determined for the simultaneous analysis of all compounds, three columns were selected and suitable chromatographic methods were optimized. Similar results were noticed for the separation of the target compounds dissolved in either meconium extract or solvent for reversed phase and ion exchange columns. However, for HILIC, the matrix had a significant influence on the peak shape and robustness of the method. Finally, the analytical methods were applied to “real” meconium samples

Extremity in horizontal and vertical Likert scale format responses. Some evidence on how visual distance between response categories influences extreme responding

In four survey experiments we show that people generally answer more extremely to survey items presented in vertical versus horizontal Likert formats. Our findings suggest that this effect may be at least partly driven by differences in the visual range spanned by the response scale (i.e. the visual distance between endpoint response categories is larger in horizontal than in a vertical format). In addition, compared to traditional horizontal Likert data, vertical Likert data contain more variance, which is mainly non-substantive. As a result, data obtained with scale formats that have different distances between response categories (as is typically the case for vertical vs. horizontal formats) may lead to differences in measurement model parameter estimates like residual terms, and in some cases factor loadings and construct correlations. Based on these results, we provide recommendations on the use of response scale formats in online surveys, bearing in mind that several online survey tool providers promote the use of vertical Likert formats and even automatically change traditional horizontal formats of Likert-type items to vertical Likert formats when viewed on small screens (e.g., on mobile phones)

MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API). MolGraph also implements a chemistry module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. To validate the GNNs, they were benchmarked against the datasets of MoleculeNet, as well as three chromatographic retention time datasets. The results on these benchmarks illustrate that the GNNs performed as expected. Additionally, the GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data. MolGraph is available at https://github.com/akensert/molgraph. Installation, tutorials and implementation details can be found at https://molgraph.readthedocs.io/en/latest/.Comment: 14 pages, 4 figures, 4 table

Customer insights for innovation:A framework and research agenda for marketing

Customer insights play a critical role in innovation. In recent years, articles studying customer insights for innovation have risen in marketing and other fields such as innovation, strategy, and entrepreneurship. However, the literature on customer insights for innovation grew fragmented and plagued by inconsistent definitions and ambiguity. The literature also lacks a precise classification of different domains of customer insights for innovation. This article offers four key contributions. First, it clearly and consistently defines customer insights for innovation. Second, it proposes a “customer insights process” that describes the activities firms and customer insights intermediaries (e.g., market research agencies) use to generate, disseminate, and apply customer insights for innovation. Third, it offers a synthesis of the knowledge on customer insights for innovation along ten domains of customer insights for innovation: (1) crowdsourcing, (2) co-creating, (3) imagining, (4) observing, (5) testing, (6) intruding, (7) interpreting, (8) organizing, (9) deciding, and (10) tracking. Fourth, the authors qualify and quantify the managerial importance and potential for scholarly research in these domains of customer insights for innovation. They conducted 12 in-depth interviews with executives at market research agencies such as Ipsos, Kantar, Nielsen, IQVIA, and GfK to do so. They surveyed 305 managers working in innovation, marketing, strategy, and customer experience. The article concludes with a research agenda for marketing aimed at igniting knowledge development in high-priority domains for customer insights for innovation.</p

Influence of pressure and temperature on molar volume and retention properties of peptides in ultra-high pressure liquid chromatography

In this study, pressure induced changes in retention were measured for model peptides possessing molecular weights between ∼1 and ∼4 kDa. The goal of the present work was to evaluate if such changes were only attributed to the variation of molar volume and if they could be estimated prior to the experiments, using theoretical models. Restrictor tubing was employed to generate pressures up to 1000 bar and experiments were conducted for mobile phase temperatures comprised between 30 and 80 °C. As expected, the retention increases significantly with pressure, up to 200% for glucagon at around 1000 bar compared to ∼100 bar. The obtained data were fitted with a theoretical model and the determination coefficients were excellent (r2 > 0.9992) for the peptides at various temperatures. On the other hand, the pressure induced change in retention was found to be temperature dependent and was more pronounced at 30 °C vs. 60 or 80 °C. Finally, using the proposed model, it was possible to easily estimate the pressure induced increase in retention for any peptide and mobile phase temperature. This allows to easily estimating the expected change in retention, when increasing the column length under UHPLC conditions